Analysis of Soft computing Techniques for face Detection

International Journal of Computer Applications, 2010

This paper describes a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates. As continual research is being conducted in the area of computer vision, one of the most practical applications under vigorous development is in the construction of a robust real-time face detection system. Successfully constructing a real-time face detection system not only implies a system capable of analyzing video streams, but also naturally leads onto the solution to the problems of extremely constraint testing environments. Analyzing a video sequence is the current challenge since faces are constantly in dynamic motion, presenting many different possible rotational and illumination conditions. While solutions to the task of face detection have been presented, detection performances of many systems are heavily dependent upon a strictly constrained environment. The problem of detecting faces under gross variations remains largely uncovered. This paper gives a face detection system which uses an image based neural network to detect face images.

Journal of Computer Science, 2006

Face detection and recognition has many applications in a variety of fields such as security system, videoconferencing and identification. Face classification is currently implemented in software. A hardware implementation allows real-time processing, but has higher cost and time to-market.

In this paper, a new approach of face detection system is developed. This system develops the algorithm for computing the accurate measurement of face features. The task of detecting and locating human faces in arbitrary images is complex due to the variability present across human faces, including skin color, pose, expression, position and orientation, and the presence of 'facial furniture' such as glasses or facial hair. In this system, a neural network-based upright frontal face detection system is presented. A retinally connected neural network examines small windows of an image, and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. A straightforward procedure for aligning positive face examples for training was presented. It uses a transformer that converts an image of human face into a feature vector, which will then be compared with the feature vectors of a training set of human faces to classify the image. Some mathematical concepts are used to calculate the distance and angles between feature points. And histogram equalization is used to enhance the selected feature. In this paper, faces are chosen because it can generate the significant features for human face than other techniques. Finally, matching is accomplished by detecting the test photo. For programming and simulation of this system, MATLAB software is applied. The neural network toolbox " nntool " is called from the main function for training system. This research develops a simple face detection system for to provide the security system.

Pattern Analysis and …, 2002

The International journal of Multimedia & Its Applications, 2014

Face detection is one of the most relevant applications of image processing and biometric systems. Artificial neural networks (ANN) have been used in the field of image processing and pattern recognition. There is lack of literature surveys which give overview about the studies and researches related to the using of ANN in face detection. Therefore, this research includes a general review of face detection studies and systems which based on different ANN approaches and algorithms. The strengths and limitations of these literature studies and systems were included also.

Face detection is the problem of determining whether there are human faces in the image and tries to make a judgment on whether or not that image contains a face. In this paper, we propose a face detector using an efficient architecture based on a Multi-Layer Perceptron (MLP) neural network and Maximal Rejection Classifier (MRC). The proposed approach significantly improves the efficiency and the accuracy of detection in comparison with the traditional neural-network techniques. In order to reduce the total computation cost, we organize the neural network in a pre-stage that is able to reject a majority of non-face patterns in the image backgrounds, thereby significantly improving the overall detection efficiency while maintaining the detection accuracy. An important advantage of the new architecture is that it has a homogeneous structure so that it is suitable for very efficient implementation using programmable devices. Comparisons with other state-of-the-art face detection systems are presented. Our proposed approach achieves one of the best detection accuracies with significantly reduced training and detection cost. .

International Journal of Nonlinear Analysis and Applications, 2022

Discovering objects and knowing their number has been discussed in many works. Face detection technology is important for the visual scene, Deep learning theory using computer technology to discover the face, which is a wide field in marketing, traffic and security system control systems, in addition to photography. Facial recognition algorithms or face detection Include steps for the facial image to extract features to match them with a database. The face has a biometric feature. The facial feature consists of prominent and easily identifiable information that is responsible for distinguishing the objects that distinguish the face, the distance between the eyes, the shape of the nose, and The mouth for the device to perform a training group and record the data. Matlab program helps to dispense with training because MATLAB provides the instruction (CascadeObjectDetector) for facial recognition and the Viola-Jones algorithm with the result of separating the detection results in the f...

2006

In this paper, a new approach to reduce the computation time taken by neural networks for the searching process is introduced. Both fast and cooperative modular neural networks are combined to enhance the performance of the detection process. Such approach is applied to identify human faces automatically in cluttered scenes. In the detection phase, neural networks are used to test whether a window of 20x20 pixels contains a face or not. The major difficulty in the learning process comes from the large database required for face / nonface images. A simple design for cooperative modular neural networks is presented to solve this problem by dividing these data into three groups. Such division results in reduction of computational complexity and thus decreasing the time and memory needed during the test of an image. Simulation results for the proposed algorithm on Bio database show a good performance.

— Human face detection and recognition applications present a great interest in the area of computer vision, with various methods and approaches that provide impressive performance. It plays important role in many applications such as video surveillance and face image database management. However, it is difficult to develop a complete robust face detector due to various light conditions, face sizes, face orientations, facial expressions, background and skin colors —In this paper, we present a feed forward back propagation based artificial neural network learning algorithm for recognizing human faces. A facial detection and recognition system has been proposed to recognize registered faces in the database and new faces that are not part of the database. Thus, when capturing a new picture, the essential step is to detect the picture and transfer it to the database where there is a pool of registered faces. Consequently, the system will respond to the new picture by either of the following two actions; first it will start by comparing this new image with the group of registered faces in the database. Accordingly, it responds by either recognizing the new picture and corresponding it with the matching face, or by identifying it as a new face that is not found in the database. The Artificial Neural Network trained by the back-propagation algorithm has 10 inputs, one hidden layer, and one output layer. Experimental results demonstrate successful face detection and recognition using the adopted algorithm.

International Journal of Computer Applications, 2010

Face detection is the problem of determining whether a subwindow of an image contains a face or not. The rapidly expanding research in face processing is based on the premise that information about a user's identity, state, and intent can be extracted from images, and that computers can then react accordingly. This hybrid face detection system is combination of two methods i.e. Feature Extraction method and Neural Networks method. This method works in two stages. The first stage involves extraction of pertinent features from the localized facial image using Gabor filters. Second stage requires classification of facial images based on the derived feature vector obtained in the previous stage. And so a Neural Network Based classifier is used which examines an incremental small window of an image to decide if there is a face contained in each window. The neural network is trained to choose between two classes "faces" and "non-faces" images. To decrease the amount of time needed for detection and to enhance image's quality, processing the image before training the neural network on these two classes enhances the algorithm. Training a neural network for the face detection task is a challenging job due to the difficulty in characterizing "non-face" images. It is easy to get a representative sample of images that contain faces but it is much harder to get a representative sample of those images that do not contain faces.